HistDAWass (version 1.0.4)

WH.regression.GOF: Goodness of Fit indices for Multiple regression of histogram variables based on a two component model and L2 Wasserstein distance

Description

It computes three goodness of fit indices using the results and the predictions of a regression done with WH.regression.two.components function.

Usage

WH.regression.GOF(observed, predicted)

Arguments

observed

A one column MatH object, the observed histogram variable

predicted

A one column MatH object, the predicted histogram variable.

Value

a list with the GOF indices

References

Irpino A, Verde R (in press 2015). Linear regression for numeric symbolic variables: a least squares approach based on Wasserstein Distance. ADVANCES IN DATA ANALYSIS AND CLASSIFICATION, ISSN: 1862-5347, DOI:10.1007/s11634-015-0197-7 An extended version is available on arXiv repository arXiv:1202.1436v2 http://arxiv.org/abs/1202.1436v2

Examples

# NOT RUN {
# do regression
 model.parameters=WH.regression.two.components(data = BLOOD,Yvar = 1, Xvars= c(2:3))
 #' # do prediction
Predicted.BLOOD=WH.regression.two.components.predict(data = BLOOD[,2:3],parameters=model.parameters)
# compute GOF indices
GOF.indices=WH.regression.GOF(observed=BLOOD[,1], predicted=Predicted.BLOOD)
# }